research
∙
09/04/2023
Composite federated learning with heterogeneous data
We propose a novel algorithm for solving the composite Federated Learnin...
share
research
∙
08/07/2023
Exact Separation of Eigenvalues of Large Dimensional Noncentral Sample Covariance Matrices
Let _n =1/n(_n + ^1/2_n _n)(_n + ^1/2_n _n)^* where _n is a p × n matrix...
research
∙
07/05/2023
Securing Cloud FPGAs Against Power Side-Channel Attacks: A Case Study on Iterative AES
The various benefits of multi-tenanting, such as higher device utilizati...
research
∙
06/07/2023
Achieving Consensus over Compact Submanifolds
We consider the consensus problem in a decentralized network, focusing o...
research
∙
04/03/2023
Two-sample test of sparse stochastic block models
The paper discusses a statistical problem related to testing for differe...
research
∙
03/31/2023
Decentralized Weakly Convex Optimization Over the Stiefel Manifold
We focus on a class of non-smooth optimization problems over the Stiefel...
research
∙
03/30/2023
KOO approach for scalable variable selection problem in large-dimensional regression
An important issue in many multivariate regression problems is to elimin...
research
∙
03/27/2023
Machine Learning for Microprocessor Performance Bug Localization
The validation process for microprocessors is a very complex task that c...
research
∙
03/25/2023
A spectral based goodness-of-fit test for stochastic block models
Community detection in complex networks has attracted considerable atten...
research
∙
03/22/2023
No Eigenvalues Outside the Support of the Limiting Spectral Distribution of Large Dimensional noncentral Sample Covariance Matrices
Let _n =1/n(_n + ^1/2_n _n)(_n + ^1/2_n _n)^*, where _n is a p × n matri...
research
∙
03/16/2023
Decentralized Riemannian natural gradient methods with Kronecker-product approximations
With a computationally efficient approximation of the second-order infor...
research
∙
02/03/2023
Analysis of the limiting spectral distribution of large dimensional General information-plus-noise type matrices
In this paper, we derive the analytical behavior of the limiting spectra...
research
∙
01/27/2023
Sampling without replacement from a high-dimensional finite population
It is well known that most of the existing theoretical results in statis...
research
∙
12/12/2022
A CLT for the LSS of large dimensional sample covariance matrices with diverging spikes
In this paper, we establish the central limit theorem (CLT) for linear s...
research
∙
07/15/2022
Riemannian Natural Gradient Methods
This paper studies large-scale optimization problems on Riemannian manif...
research
∙
07/13/2022
Spectral Statistics of Sample Block Correlation Matrices
A fundamental concept in multivariate statistics, sample correlation mat...
research
∙
05/15/2022
A CLT for the LSS of large dimensional sample covariance matrices with unbounded dispersions
In this paper, we establish the central limit theorem (CLT) for linear s...
research
∙
03/30/2022
Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data
Applying machine learning (ML) in design flow is a popular trend in EDA ...
research
∙
01/28/2022
The limiting spectral distribution of large dimensional general information-plus-noise type matrices
Let X_n be n× N random complex matrices, R_n and T_n be non-random compl...
research
∙
04/10/2021
Spiked eigenvalues of noncentral Fisher matrix with applications
In this paper, we investigate the asymptotic behavior of spiked eigenval...
research
∙
02/19/2021
Toward Taming the Overhead Monster for Data-Flow Integrity
Data-Flow Integrity (DFI) is a well-known approach to effectively detect...
research
∙
12/03/2020
Automatic Routability Predictor Development Using Neural Architecture Search
The rise of machine learning technology inspires a boom of its applicati...
research
∙
11/27/2020
Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation
Net length is a key proxy metric for optimizing timing and power across ...
research
∙
11/26/2020
PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network
IR drop is a fundamental constraint required by almost all chip designs....
research
∙
11/26/2020
FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning
Design flow parameters are of utmost importance to chip design quality a...
research
∙
11/26/2020
Fast IR Drop Estimation with Machine Learning
IR drop constraint is a fundamental requirement enforced in almost all c...
research
∙
11/17/2020
Automatic Microprocessor Performance Bug Detection
Processor design validation and debug is a difficult and complex task, w...
research
∙
08/24/2020
ALIGN: A System for Automating Analog Layout
ALIGN ("Analog Layout, Intelligently Generated from Netlists") is an ope...
research
∙
01/02/2020
Modified Pillai's trace statistics for two high-dimensional sample covariance matrices
The goal of this study was to test the equality of two covariance matric...
research
∙
12/06/2019
The limits of the sample spiked eigenvalues for a high-dimensional generalized Fisher matrix and its applications
A generalized spiked Fisher matrix is considered in this paper. We estab...
research
∙
06/16/2019
Community Detection Based on the L_∞ convergence of eigenvectors in DCBM
Spectral clustering is one of the most popular algorithms for community ...
research
∙
10/30/2018
Strong consistency of the AIC, BIC, C_p and KOO methods in high-dimensional multivariate linear regression
Variable selection is essential for improving inference and interpretati...
research
∙
03/21/2018
